40

SNR/RP Aware Routing Model for MANETs

Fuad Alnajjar, City College and Graduate Center of City University of New York

Abstract—Design a service-quality aware routing algorithm in

mobile ad hoc network (MANET) is difficult due to the nature of

the environment where nodes are mobile and connectivity is

intermittent that change topology rapidly. In this work, we

propose cross-layer design to attain a reliable data transmission

in MANET. In MANET environment challenge is to design a

mechanism that can provide high quality of service with a high

level of performance or to achieve service quality in terms of high

delivery rate, low latency and low bit error. The key components

of our approach include a cross-layer design (CLD) to improve

information sharing between network and physical layers. We

present a model that allows the network layer to adjust its

routing protocol dynamically based on signal noise ratio (SNR)

and received power (RP) along the end-to-end routing path for

each transmission link to improve the end-to-end routing

performance in MANET. We evaluate our model using well

known MANET - routing protocols: AODV, DSR, OLSR to

illustrate that our CLD improved their performances with

respect to service quality. We analyze their performance in terms

of: packet delivery rate, average end-to-end delay and overhead.

Index Terms—Cross Layer Design, MANET, Routing Protocols,

QoS, SNR & OPNET.

I. INTRODUCTION

A Mobile Ad hoc Network (MANET) is a dynamic wireless

network with or without fixed infrastructure. Nodes may move

freely and arrange themselves randomly. The contacts

between nodes in the network do not occur very frequently. As

a result, the network graph is rarely, if ever, connected and

message delivery required a mechanism to deal with this

environment [1]

Routing in MANET using the shortest-path metric is not a

sufficient condition to construct high-quality paths, because

minimum hop count routing often chooses routes that have

significantly less capacity than the best paths that exist in the

network. [2]

Most of the existing MANET protocols optimize hop count

to build a route selection. Examples of MANET protocols are

Ad hoc On Demand Distance Vector (AODV) [3], Dynamic

Source Routing (DSR) [4], and Optimized Link State Routing

Protocol (OLSR) [5]. However, the routes selected based on

hop count alone may be characterized with bad quality since

the routing protocols do not ignore weak quality links which

are typically used to connect to remote nodes. These links

usually have poor signal-to-noise ratio (SNR), hence higher

frame error rates and lower throughput. [6], [7].

The wireless channel quality among mobile nodes is time

varying due to fading, Doppler Effect and pathloss. Known

that the shortest-path metric does not take into account the

physical channel variations of the wireless medium, it is

desirable to choose the route with minimum cost based on

some other metrics which are aware of the wireless nature of

the underlying physical channel. In MANET, there are many

other metrics to be taking into account: power, SNR, packet

loss, maximum available bandwidth etc. These metrics should

come from a cross-layer approach in order to make the routing

layer aware of the local issues of the underling layers. [8].

The ability of MANET to provide acceptable quality of

service (QoS) is restricted by the ability of the underlying

routing protocol to provide consistent behavior despite the

inherent dynamics of a mobile computing environment. [9]

[10].

Cross-Layer Design has enormous potential in wireless

communication systems. By using Cross Layer Design (CLD)

we try to offer dedicated QoS for dedicated applications.

Our objective is to design a mechanism to provide an

efficient QoS routing protocol to enhance the performance of

existing routing protocols in Mobile ad hoc network

environment.

In this paper we select AODV, DSR and OLSR as common

MANET routing protocols to demonstrate our two models,

Signal to noise Ratio (SNR) and Received Power (RP), to

enhance the quality of service of those protocols. We evaluate

how the protocols differ in the methods they use to select

paths, detect broken links, and buffer messages during periods

of link outage. Our new approach is called Signal to Noise

Ratio/Received Power Aware Routing Algorithm (SNR/RP).

We computed differences in terms of packet delivery ratio,

throughput, end-to-end latency, and overhead. We show that

the performances of AODV, DSR, and OLSR protocols

improved by using the proposed model.

The rest of this paper is organized as follows: Section II

discusses related work. Section III gives background about

selected routing protocols. Section IV presents the proposed

cross layer design and model optimization. Section V

discusses simulation environment setup. Section VI discusses

simulation results and finally Section VII concludes the paper

and Section. VIII presents our future work.

II. RELATED WORK

Many proposals and models addressed quality of service

(QoS) among mobile nodes of the wireless networks and

considered the link quality in their designs and architectures.

Wisitpongphan and et al. [11] proposed a bit error rate

(BER)-based routing design, where the chosen route is the one

which guarantees the lowest BER at the ending node. They

considered providing QoS in terms of BER at the destination

node.

[12] presented a mechanism to improve both the routing and

data forwarding performance of DSR, with lesser power

consumption. This mechanism involves intelligent use of the

route discovery and route maintenance process thereby

providing faster routing and reduced traffic as compared to the

basic DSR. This mechanism enables faster data forwarding

and reduced collisions with lesser power consumption.

Cyber Journals: Multidisciplinary Journals in Science and Technology, Journal of Selected Areas in Telecommunications (JSAT), March Edition, 2011

41

In [8] authors modified DSR to work as three-state Markov

model of the wireless channel instead of two-state Markov

model (Gilbert-Elliot model) by applying a higher order of

Markov chains. They applied their model to the Dynamic

Source Routing protocol (DSR). In their proposed modified

DSR, both the route discovery and route selection are based on

physical layer parameter and the link monitoring function

located at each node.

Authors in [13] proposed a simple extension of DSR. They

presented a model to reduce routing overhead in request

process and the anycast group management protocol is

discussed.

In [18] work proposes using of link lifetime and channel

quality as metrics in the selection of routes. They applied the

model to the Optimized Link State Routing (OLSR) routing

protocol and focused on multipoint relay (MPR) selection

method, to find the most optimal routes between any pair of

nodes.

III. MANET ROUTING PROTOCOLS

In MANET the entire network is mobile where nodes move

freely and topology is changing rapidly because of weather,

terrain, highly variable delay links and error rate links. Nodes

may not be able to communicate directly and have to rely on

each other in order to deliver packets. The contacts between

nodes in the network do not occur very frequently that makes

routing difficult because the network graph is episodically

connected. A lot of routing algorithms have been proposed for

MANET environment and some of them have been widely

used. [19-20].

In this section we review AODV, DSR and OLSR as

selected MANET routing used in our design evaluation.

Ad Hoc On-demand Distance Vector Routing (AODV)

protocol [3] is a reactive routing protocol. As a reactive

routing protocol, it maintains only routing information about

the active paths. Every node uses hello messages to notify its

existence to its neighbors and maintains routing information in

their routing tables to keep a next-hop routing table that

contains the destinations to which it has a route. In AODV,

when a source node wants to send packets to the destination

but no route is available, it initiates a route discovery

operation. In the route discovery operation, the source

broadcasts route request (RREQ) packets. A RREQ includes

addresses of the source and the destination, the broadcast ID,

the last seen sequence number of the destination as well as the

source node’s sequence number. OLSR uses sequences

numbers to ensure loop-free and up-to-date routes. Each

RREQ has Time-to-Live (TTL) and nodes maintain a cache to

keep track of RREQs it has received and discards any RREQ

has seen before. When intermediate or destination node

receives RREQ, it checks destination sequence numbers to

what it knows. Then, the node creates a route reply (RREP)

packet and forwards back to the source node only if the

destination sequence number is equal to or greater than the

one specified in RREQ. The RREP follows the reverse path of

the respective RREP and intermediate nodes update their next-

hop table entries with respect to the destination node. When a

node discovers a link disconnection, it broadcasts a route error

(RERR) packet to its neighbors, which in turn propagates the

RERR packet towards nodes whose routes may be affected by

the disconnected link. Then, the affected source can re-initiate

a route discovery operation if the route is still needed. [20]

Dynamic Source Routing (DSR) [4] stands as one of the

common representatives of reactive routing protocols like all

On-Demand routing algorithms, AODV, Dynamic MANET

On-demand (DYMO). DSR applies source routing rather than

hop-by-hop routing, in which each packet to be routed

carrying in its header the full ordered list of nodes through

which the packet should pass. The key benefits of source

routing is that intermediate nodes do not need to maintain up-

to-date routing information in order to route the packets they

forward, since the packets themselves already contain all the

routing decisions. This fact, coupled with the on-demand

nature of the protocol, eliminates the need for the periodic

route advertisement and neighbor detection packets present in

other protocols. In DSR source node generates a route request

packet when it has a new route to a destination. The route

request is flooded through the network until it reaches some

nodes with a route to that destination. Each route request

packet holds the information of the route it has propagated.

When the route request packet arrives at the destination or an

intermediate node with a route to the destination, a route reply

packet will be generated. This reply packet is then sent back to

the source node following the reverse route contained in the

route request packet. While transmitting the data traffic, the

complete path is added to each data packet according to the

routing table of the source node. The intermediate nodes

forward packets according to the path provided in the packet.

More clearly, in DSR routing protocol to send route reply

packet, when current route breaks, destination seeks a new

route. [14, 19- 21].

The Optimized Link State Routing protocol (OLSR) [5, 18]

is a proactive routing protocol and operates as a table driven

protocol. In OLSR, each node exchanges its link state

information to all other nodes in the network and transmits its

neighbor list regularly so nodes can know their two hops

neighbors. Each node selects its multipoint relay (MPR) and

the MPR nodes announce this information periodically using

Topology control (TC) messages. When a node broadcasts a

message, its neighbors will receive the message. The protocol

uses MPRs to facilitate flooding of control messages and only

the MPRs that have not seen the message before, rebroadcast

the message in the network periodically. MPRs are used as

intermediate nodes to route packets. Then, each node floods

the link state information of its MPRs through the network and

it obtains network topology information and constructs its

routing table through link state messages. [20].

In this work we try to change route selection mechanism.

We define a signal to noise ratio (SNR0 and received power

(RP) parameters as new metrics in which those values are

considered in constructing routes. Given those features, source

node can select the best and more stable route out of various

available routes based on Signal to Noise Ratio (SNR) or

Received Power (RP) not number of hops or shortest path. In

this work our aim is improving the Quality of Service (QoS)

and the performance of the routing protocols in MANET

environment.

42

IV. SNR/RP AWARE ROUTING MODEL

Routing in MANET is difficult due to the dynamic nature of

network topology and the resource constraints. The issue of

Link reliability in mobile ad hoc networks is a main problem

to transmit messages through the wireless channels. Routing in

multi-hop wireless networks using the shortest-path metric is

not an adequate condition to build good quality paths, because

minimum hop count routing often selects paths that have

significantly less capacity than the best paths that exist in the

network. [2]

Physical-layer limits of wireless channel because of: time-

varying fading, multipath, co-channel interference, hostile

jamming, mobility, dynamic network topology.

In technicality, information from the transmission links,

such as Signal to Noise Ratio (SNR) and Received Power

(RP), can furnish valuable information to the source node

about the transmission paths as far as routing is concerned.

Each wireless node can communicate with any other node

within its transmission range, which depends on SNR and RP

at the receiver node.

In our work we used OPNET simulator [15]. We modified

the packet formats in OPNET simulator of AODV (figure 1),

DSR (figure 2) and OLSR (figure 3) and added two extra

fields to store the worst value of power strength (received

power strength) and worst value of SNR (signal-to-noise ratio)

along the route from destination to source.

Figure 1: Modified Route Reply packet format in OPNET of AODV including

metrics of SNR and RP.

Figure 2: Modified Route Reply packet format of DSR including metrics of

SNR and RP.

Figure 3: Modified packet format of OLSR to include metrics of SNR and RP.

Section 3 illustrated how original AODV, DSR and OLSR

work. We modified also the mechanism of those routing

protocols processes to include our SNR/RP model.

A. Modification in AODV and DSR (Reactive routing)

In case of DSR and AODV, the new mechanism will work

as follows: when the route request packet arrives at the

destination or an intermediate node with a route to the

destination, a route reply packet will be generated. This reply

packet is then sent back to the source node following the

reverse route contained in the route request packet. Each

intermediate node will update the SNR and RP values if its

link values of SNR and RP lower than the existing recorded

values in the route reply packet. If SNR/RP values of its link

are greater than recorded value, the node will not update the

value. The process will continue until the route reply packet

reach the source node. Now, at the source node there are many

of available routes with different values of SNR and RP. The

Source node will select the route based on the value of best of

worse available values of SNR or RP. Figure 4 demonstrates

the flow chart of how modified DSR and AODV routing

protocols work after implementing the SNR/RP model.

Dotted-line areas in the figure represent new process. [21].

Figure 4. Flow chart shows how SNR/RP model works with DSR and AODV.

B. Modification in OLSR (Proactive routing)

Original OLSR uses hello and Topology Control (TC)

messages to discover and exchange link state information

throughout the network. Nodes compute next hop destination

by using topology information received by neighbors

considering shortest hop forwarding paths. OLSR makes use

of "Hello" messages to find its one hop neighbors and its two

hop neighbors through their responses. The sender node can

then select its MPR based on the one hop node that offers the

best routes to the two hop nodes.

In our SNR and RP model, we modified the selection

process of MPR and makes nodes select MPR based on the

SNR and RP values of each link connected to those MPR

UDP Header Overhead

(64 bits)

Options

(0 bits) Received

Power

(8 bits)

SNR

(8 bits)

Received

Power

(8 bits)

SNR

(8 bits)

Packet Length

(16 bits)

Packet Sequence Number

(16 bits)

Message

(0 bits)

43

instead of the shortest paths. Modified OLSR constructs

routing table for each node using the SNR/RP to guarantee the

quality of service in the network.

Figure 5 illustrates the mechanism of our new approach,

SNR/RP aware routing algorithm when it applies to DSR,

AODV and OLSR routing protocols. The values on links

represent the values of Signal to Noise Ratio of the link or

values of received power of the link. When node S needs to

send a packet to node R. Node S sends 2 route request packets

along path 1 and path 2. Node R generates 2 route reply

packets to node S along the reverse routes of paths 1 and 2.

Now, at node S there 2 available routes to destination R, path

1 with 5 hops but the lowest value of SNR or RP found in the

end-to-end path is 3, and path 2 with 4 hops but the lowest

value of SNR or RP found in the end-to-end path is 2. Source

node S will sort the two routes and select path 1 based on our

new mechanism since the best worse value of path 1 is 3 is

grater than the worse value of the other path which is 2.

Traditional DSR, AODV and OLSR protocols will select Path

2 that has minimum number of hops eventhough the path has

low-quality of service.

Figure 5: Scenario shows that modified DSR and AODV with SNR/RP will

select path 1 (High QoS) rather than path 2 (minimum number of hops).

Wireless channels have high channel bit error rate and

limited bandwidth. The high bit error rate degrades the quality

of transmission and the network performance. A routing

protocol that cannot quickly recover from link breakage

caused by mobility renders a QoS model incapable of meeting

delivery requirements. [9]. Implementing our model will

guarantee the Quality of service in the environment of

MANET where is QoS is low. Any routing protocol should be

smart enough to pick a stable and good quality communication

route in order to avoid any unnecessary packet loss.

Routing in MANET is challenging due to the dynamic

nature of network topology and the resource constraints. In

our model, we create a mechanism that can provide good

delivery performance and high quality of service in MANET

environment that characterized with intermittent network and

episodically connected and nodes get intermittently connected

because of nodes mobility, terrain, weather, and jamming to

reach a reliable data transmission.

V. SIMULATION ENVIRONMENT

Our cross-layer model described above was implemented

and evaluated in OPNET v 14.5 simulator [15]. Figure 6

shows snapshot of our model used in OPNET simulator. Table

1 shows the parameters used in our simulation.

The fading modules contributed in [16] are included into

account. The modulation, BPSK, compute the BER under

fading condition from the loop-up tables. We calculate the

Doppler shift velocity according to the ground speed, pitch,

and yaw of the transmitting node and the receiving node. Look

up the fading amplitude according to the Rician K=5 factor.

[17]. we consider in our network topology to include fading,

Doppler Effect, various speed mobility.

Figure 6. Snapshot of network design in OPNET simulator.

TABLE I

SIMULATION SETUP

Parameters Value

Network Size 3 x 3 Km

Modulation Scheme BPSK

Traffic rate 11 Mbps

Transmit Power 35 mW

Packet Reception-Power

Threshold -75 dBm

Mobility model Random-Waypoint

Propagation–Path loss Free space

Propagation fading model Rayleigh, Rician

Rician K Factor 5

MAC protocol 802.11

Packet size 1024 bits

Routing protocol AODV, DSR, OLSR

Carrier frequency 2.4 GHz

Nodes number 100

Transmission Range 300 - 400 m

Speed of nodes 3, 6, 9, 12 m/s

VI. RESULTS

Simulation results evaluate the performance of AODV, DSR

and OLSR respectively, in terms of delay, traffic received,

routing traffic received (overhead), throughput and

retransmission attempts.

A

7

2 5 4

5

4 6

7

3

9

Path 2

Path 1

B D E

S R

W X Y Z

SNR/RP of link

C

5

44

A. AODV evaluation

Figure7.1 shows that traditional AODV and AODV-SNR

model provide good performance in terms of delay. Figure 7.2

illustrate that the RP model enhance the performance of

traditional AODV and increase packet delivery in the network.

7.3 shows that overhead reduced in the network with

implementing the SNR and RP model separately with AODV.

In terms of MAC layer throughput performance, figure 7.4

shows that traditional AODV, SNR model and RP model

provide same performance. Finally, figure 7.5 shows that the

SNR model and RP model reduce the retransmission attempt

in layer 2.

Figure 7.1. AODV and SNR model provide low delay in the network.

Figure 7.2. RP model increases the packet delivery.

B. DSR evaluation

It is immediately evident from the results given in figure 8.1

that delay reduced when SNR or RP models used. Figure 8.2

shows that the traditional DSR and RP model perform equally

with respect to packet delivery in the network. 8.3 illustrates

that overhead reduced in the network with implementing the

SNR and RP model separately with DSR. In terms of MAC

layer throughput performance, figure 8.4 shows that traditional

RP model provide excellent performance. Finally, figure 8.5

illustrates that the SNR model and RP model reduce the

retransmission attempt in layer 2.

Figure 7.3. RP & SNR models reduce overhead

Figure 7.4. Traditional AODV, SNR and RP models have same

throughput performance

Figure 7.5. SNR & RP models improve numbers of destination’s repliers

45

Figure 8.1. SNR & RP models reduce delay

Figure 8.2. DSR & RP model provide good performance in terms of

packet delivery

Figure 8.3. SNR & RP models reduce overhead

Figure 8.4. RP model increase layer 2 throughput

Figure 8.5. SNR & RP models reduced number of errors sent

C. OLSR evaluation

Figures 9.1, 9.2 and 9.3 show that traditional OLSR

outperforms OLSR-SNR model and OLSR-RP in terms of

delay, packet delivery and overhead. For MAC layer

throughput performance, figure 9.4 shows that traditional

OLSR, SNR model and RP model provide better performance

than OLSR. Figure 9.5 shows that OLSR, SNR model and RP

model same performance in terms of retransmission attempt.

D. General evaluation

We evaluate the performance of AODV, DSR and OLSR in

terms of delivery rate with respect to time and number of

nodes.

Figure 10.1 shows that AODV-RP increases the delivery

rate. In figure 10.2, SNR and RP models enhance the delivery

rate when time increases. Figure 10.3 illustrates that OLSR

delivery rate is higher than the models.

46

Figure 9.1. traditional OLSR provides low delay

Figure 9.2. traditional OLSR delivers more traffic

Figure 9.3. overhead in traditional OLSR is low

Figure 9.4. SNR & RP models increase throughput

Figure 9.5. Identical performance in terms of retransmission attempts

Delivery Rate

0.05

0.07

0.09

0.11

0.13

0.15

0.17

0.19

0.21

0.23

0252504756

10081260151217642016226825202772302432763528

Time (sec)

AODV AODV_RP AODV_SNR

Figure 10.1. APDV-RP model increases delivery rate

47

Delivery Rate

0.2

0.22

0.24

0.26

0.28

0.3

0.32

0252504756

1008

1260

1512

1764

2016

2268

2520

2772

3024

3276

3528

Time (sec)

DSR DSR_RP DSR_SNR

Figure 10.2. SNR & RP models presents better performance than

traditional DSR

Delivery Rate

0.3

0.32

0.34

0.36

0.38

0.4

0.42

0.44

0.46

0.48

0.5

0.52

Time (sec)

OLSR OLSR_RP OLSR_SNR

Figure 10.3. Traditional OLSR delivers more packets

Figures 11.1, 11.2 and 11.3 evaluate delivery rate with

respect to number of nodes. In figure 11.1 when number of

nodes increases AODV-SNR model increases delivery date

and outperforms traditional AODV. Figure 11.2 shows that

DSR and models achieve approximately same performance. In

figure 11.3, OLSR-RP presents high performance than other

with small number of nodes.

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

5 10 25 50 100

No. of nodes

Delivery Rate

AODV AODV_RP AODV_SNR

Figure 11.1. AODV-RP model increases delivery rate when No. nodes

increases.

0

0.15

0.3

0.45

0.6

0.75

5 10 25 50 100

No. of nodes

Delivery Rate

DSR DSR_RP DSR_SNR

Figure 11.2. when No. nodes increases DSR and models have same

performance

0

0.1

0.2

0.3

0.4

0.5

0.6

5 10 25 50 100

No. of nodes

Delivery Rate

OLSR OLSR_RP OLSR_SNR

Figure 11.3. OLSR-RP presents good performance with small group of nodes

VII. DISCUSSION AND CONCLUSIONS

In this work, we present our Cross-Layer Design (CLD) to

improve the performance of well known MANET routing

protocols, AODV, DSR and OLSR. We modified the

protocols to choose routes according to the Signal to Noise

Ratio (SNR) or a Received Power (RP) criterion which is

characterized with the best value of SNR or RP of the weakest

link along the route from destination to source to eliminate the

routes with bad links that has very low SNR and to improve

QoS. We have presented our recent results of the SNR/RP

aware routing design to achieve reliable communication in

networks associated with intermittent connectivity. The

challenge was to find a routing design that can deal with

dynamic environment causing networks to split and merge,

considering nodes mobility, fading, and Doppler Effect.

Simulation results present performance evaluation of the

protocols with our CLD model. The evaluation illustrates how

those protocols act in the network with and without our CLD

model in terms of various network behaviors.

VIII. FUTURE WORK

We intend to continue on developing the proposed model

and provide a detailed analytical as well as simulation-based

study. Our future work will complete the research to

implement SNR/RP aware routing design on GRP and TORA.

48

Also, we will implement Delay/Disruption Tolerant Network

(DTN) in our Model in OPNET simulator to study and analyze

the impact of the physical layer parameters on the

performance of DTN routing protocols. Also, our future work

will complete the research by implement DTN based routing

algorithms in Aerial/terrestrial Airborne Network

environment.

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